Speech production is studied from both psycholinguistic and motor-control perspectives, with little interaction between the approaches. We assessed the explanatory value of integrating psycholinguistic and motor-control concepts for theories of speech production. By augmenting a popular psycholinguistic model of lexical retrieval with a motor-control-inspired architecture, we created a new computational model to explain speech errors in the context of aphasia. Comparing the model fits to picture-naming data from 255 aphasic patients, we found that our new model improves fits for a theoretically predictable subtype of aphasia: conduction. We discovered that the improved fits for this group were a result of strong auditory-lexical feedback activation, combined with weaker auditory-motor feedforward activation, leading to increased competition from phonologically related neighbors during lexical selection. We discuss the implications of our findings with respect to other extant models of lexical retrieval.
Keywords: Computational models; Neuropsychology; Speech production.